Regression
sub_df <- df[,c('Mean_NTL',"X2018_Murde","Pop_Dens_1","Employ_R_1","Poverty__1","Educatio_1","Median_I_1","House_De_1","Vacant_R_1","House_Va_1","Room_Num_1","X2018_Motor","X2018_Larce","X2018_Prope","X2018_Rape","X2018_Murde")]
sub_df
library(car)
full.model <- lm(sub_df$X2018_Murde.1 ~sub_df$Mean_NTL + sub_df$Pop_Dens_1 + sub_df$Employ_R_1 + sub_df$Poverty__1 + sub_df$Educatio_1 + sub_df$Median_I_1 + sub_df$House_De_1 + sub_df$Vacant_R_1 + sub_df$House_Va_1 + sub_df$Room_Num_1)
reduced.model <- step(full.model, direction = 'backward')
Start: AIC=1396.79
sub_df$X2018_Murde.1 ~ sub_df$Mean_NTL + sub_df$Pop_Dens_1 +
sub_df$Employ_R_1 + sub_df$Poverty__1 + sub_df$Educatio_1 +
sub_df$Median_I_1 + sub_df$House_De_1 + sub_df$Vacant_R_1 +
sub_df$House_Va_1 + sub_df$Room_Num_1
Df Sum of Sq RSS AIC
- sub_df$House_De_1 1 4649 1605966 1395.2
- sub_df$House_Va_1 1 8349 1609665 1395.6
<none> 1601316 1396.8
- sub_df$Vacant_R_1 1 36966 1638282 1398.2
- sub_df$Poverty__1 1 50366 1651682 1399.4
- sub_df$Employ_R_1 1 103504 1704820 1404.1
- sub_df$Pop_Dens_1 1 110474 1711791 1404.7
- sub_df$Educatio_1 1 150665 1751982 1408.1
- sub_df$Median_I_1 1 186520 1787836 1411.1
- sub_df$Room_Num_1 1 247568 1848884 1416.1
- sub_df$Mean_NTL 1 533447 2134763 1437.3
Step: AIC=1395.22
sub_df$X2018_Murde.1 ~ sub_df$Mean_NTL + sub_df$Pop_Dens_1 +
sub_df$Employ_R_1 + sub_df$Poverty__1 + sub_df$Educatio_1 +
sub_df$Median_I_1 + sub_df$Vacant_R_1 + sub_df$House_Va_1 +
sub_df$Room_Num_1
Df Sum of Sq RSS AIC
- sub_df$House_Va_1 1 12100 1618066 1394.3
<none> 1605966 1395.2
- sub_df$Vacant_R_1 1 32868 1638834 1396.2
- sub_df$Poverty__1 1 46712 1652678 1397.5
- sub_df$Employ_R_1 1 103232 1709197 1402.4
- sub_df$Pop_Dens_1 1 140202 1746168 1405.6
- sub_df$Educatio_1 1 151193 1757158 1406.5
- sub_df$Median_I_1 1 192285 1798251 1410.0
- sub_df$Room_Num_1 1 243340 1849305 1414.1
- sub_df$Mean_NTL 1 565638 2171603 1437.9
Step: AIC=1394.33
sub_df$X2018_Murde.1 ~ sub_df$Mean_NTL + sub_df$Pop_Dens_1 +
sub_df$Employ_R_1 + sub_df$Poverty__1 + sub_df$Educatio_1 +
sub_df$Median_I_1 + sub_df$Vacant_R_1 + sub_df$Room_Num_1
Df Sum of Sq RSS AIC
<none> 1618066 1394.3
- sub_df$Vacant_R_1 1 35808 1653874 1395.6
- sub_df$Poverty__1 1 40946 1659012 1396.0
- sub_df$Employ_R_1 1 94801 1712867 1400.8
- sub_df$Pop_Dens_1 1 135438 1753504 1404.2
- sub_df$Educatio_1 1 141551 1759617 1404.7
- sub_df$Median_I_1 1 221933 1839999 1411.3
- sub_df$Room_Num_1 1 251779 1869845 1413.7
- sub_df$Mean_NTL 1 748308 2366374 1448.6
reduced.model
Call:
lm(formula = sub_df$X2018_Murde.1 ~ sub_df$Mean_NTL + sub_df$Pop_Dens_1 +
sub_df$Employ_R_1 + sub_df$Poverty__1 + sub_df$Educatio_1 +
sub_df$Median_I_1 + sub_df$Vacant_R_1 + sub_df$Room_Num_1)
Coefficients:
(Intercept) sub_df$Mean_NTL sub_df$Pop_Dens_1
-296.00388 4.12917 -0.53812
sub_df$Employ_R_1 sub_df$Poverty__1 sub_df$Educatio_1
50.41677 -11.31568 -39.52070
sub_df$Median_I_1 sub_df$Vacant_R_1 sub_df$Room_Num_1
-0.00346 327.33486 87.60889
full.model <- lm(sub_df$X2018_Rape~sub_df$Mean_NTL + sub_df$Pop_Dens_1 + sub_df$Employ_R_1 + sub_df$Poverty__1 + sub_df$Educatio_1 + sub_df$Median_I_1 + sub_df$House_De_1 + sub_df$Vacant_R_1 + sub_df$House_Va_1 + sub_df$Room_Num_1)
reduced.model <- step(full.model, direction = 'backward')
Start: AIC=1312.12
sub_df$X2018_Rape ~ sub_df$Mean_NTL + sub_df$Pop_Dens_1 + sub_df$Employ_R_1 +
sub_df$Poverty__1 + sub_df$Educatio_1 + sub_df$Median_I_1 +
sub_df$House_De_1 + sub_df$Vacant_R_1 + sub_df$House_Va_1 +
sub_df$Room_Num_1
Df Sum of Sq RSS AIC
- sub_df$Mean_NTL 1 293.4 903986 1310.2
- sub_df$Room_Num_1 1 3144.1 906836 1310.6
- sub_df$Educatio_1 1 6511.2 910203 1311.2
- sub_df$House_Va_1 1 8232.3 911925 1311.5
<none> 903692 1312.1
- sub_df$Median_I_1 1 13927.9 917620 1312.4
- sub_df$Vacant_R_1 1 14056.9 917749 1312.4
- sub_df$Poverty__1 1 14631.6 918324 1312.5
- sub_df$Employ_R_1 1 17481.4 921174 1313.0
- sub_df$Pop_Dens_1 1 22732.0 926424 1313.8
- sub_df$House_De_1 1 23170.2 926862 1313.9
Step: AIC=1310.17
sub_df$X2018_Rape ~ sub_df$Pop_Dens_1 + sub_df$Employ_R_1 + sub_df$Poverty__1 +
sub_df$Educatio_1 + sub_df$Median_I_1 + sub_df$House_De_1 +
sub_df$Vacant_R_1 + sub_df$House_Va_1 + sub_df$Room_Num_1
Df Sum of Sq RSS AIC
- sub_df$Room_Num_1 1 4049.4 908035 1308.8
- sub_df$Educatio_1 1 6598.9 910585 1309.2
- sub_df$House_Va_1 1 10533.2 914519 1309.9
<none> 903986 1310.2
- sub_df$Median_I_1 1 13673.9 917660 1310.4
- sub_df$Vacant_R_1 1 13765.5 917751 1310.4
- sub_df$Poverty__1 1 14347.9 918334 1310.5
- sub_df$Employ_R_1 1 17400.2 921386 1311.0
- sub_df$House_De_1 1 22944.4 926930 1311.9
- sub_df$Pop_Dens_1 1 24473.7 928459 1312.1
Step: AIC=1308.83
sub_df$X2018_Rape ~ sub_df$Pop_Dens_1 + sub_df$Employ_R_1 + sub_df$Poverty__1 +
sub_df$Educatio_1 + sub_df$Median_I_1 + sub_df$House_De_1 +
sub_df$Vacant_R_1 + sub_df$House_Va_1
Df Sum of Sq RSS AIC
- sub_df$Educatio_1 1 9332.8 917368 1308.3
- sub_df$Median_I_1 1 9769.3 917804 1308.4
- sub_df$Vacant_R_1 1 10212.7 918248 1308.5
- sub_df$House_Va_1 1 10421.1 918456 1308.5
- sub_df$Poverty__1 1 11260.3 919295 1308.7
<none> 908035 1308.8
- sub_df$Employ_R_1 1 14393.1 922428 1309.2
- sub_df$House_De_1 1 19700.0 927735 1310.0
- sub_df$Pop_Dens_1 1 24906.8 932942 1310.8
Step: AIC=1308.34
sub_df$X2018_Rape ~ sub_df$Pop_Dens_1 + sub_df$Employ_R_1 + sub_df$Poverty__1 +
sub_df$Median_I_1 + sub_df$House_De_1 + sub_df$Vacant_R_1 +
sub_df$House_Va_1
Df Sum of Sq RSS AIC
- sub_df$Employ_R_1 1 6223.9 923592 1307.3
- sub_df$Poverty__1 1 6651.1 924019 1307.4
- sub_df$Vacant_R_1 1 10786.3 928154 1308.1
<none> 917368 1308.3
- sub_df$House_Va_1 1 14680.9 932049 1308.7
- sub_df$Median_I_1 1 16574.8 933943 1309.0
- sub_df$House_De_1 1 18484.0 935852 1309.3
- sub_df$Pop_Dens_1 1 24285.2 941653 1310.2
Step: AIC=1307.34
sub_df$X2018_Rape ~ sub_df$Pop_Dens_1 + sub_df$Poverty__1 + sub_df$Median_I_1 +
sub_df$House_De_1 + sub_df$Vacant_R_1 + sub_df$House_Va_1
Df Sum of Sq RSS AIC
- sub_df$Poverty__1 1 479.6 924071 1305.4
- sub_df$Vacant_R_1 1 11977.0 935569 1307.2
<none> 923592 1307.3
- sub_df$Median_I_1 1 13944.3 937536 1307.6
- sub_df$House_Va_1 1 16182.9 939775 1307.9
- sub_df$House_De_1 1 21024.2 944616 1308.7
- sub_df$Pop_Dens_1 1 23432.8 947025 1309.0
Step: AIC=1305.42
sub_df$X2018_Rape ~ sub_df$Pop_Dens_1 + sub_df$Median_I_1 + sub_df$House_De_1 +
sub_df$Vacant_R_1 + sub_df$House_Va_1
Df Sum of Sq RSS AIC
<none> 924071 1305.4
- sub_df$Vacant_R_1 1 12750 936822 1305.5
- sub_df$Median_I_1 1 14139 938210 1305.7
- sub_df$House_Va_1 1 15727 939798 1305.9
- sub_df$House_De_1 1 23978 948049 1307.2
- sub_df$Pop_Dens_1 1 29295 953367 1308.0
reduced.model
Call:
lm(formula = sub_df$X2018_Rape ~ sub_df$Pop_Dens_1 + sub_df$Median_I_1 +
sub_df$House_De_1 + sub_df$Vacant_R_1 + sub_df$House_Va_1)
Coefficients:
(Intercept) sub_df$Pop_Dens_1 sub_df$Median_I_1
2.324e+02 -2.622e-01 -8.125e-04
sub_df$House_De_1 sub_df$Vacant_R_1 sub_df$House_Va_1
5.398e-02 1.840e+02 -1.589e-04
full.model <- lm(sub_df$X2018_Prope~sub_df$Mean_NTL + sub_df$Pop_Dens_1 + sub_df$Employ_R_1 + sub_df$Poverty__1 + sub_df$Educatio_1 + sub_df$Median_I_1 + sub_df$House_De_1 + sub_df$Vacant_R_1 + sub_df$House_Va_1 + sub_df$Room_Num_1)
reduced.model <- step(full.model, direction = 'backward')
Start: AIC=1071.99
sub_df$X2018_Prope ~ sub_df$Mean_NTL + sub_df$Pop_Dens_1 + sub_df$Employ_R_1 +
sub_df$Poverty__1 + sub_df$Educatio_1 + sub_df$Median_I_1 +
sub_df$House_De_1 + sub_df$Vacant_R_1 + sub_df$House_Va_1 +
sub_df$Room_Num_1
Df Sum of Sq RSS AIC
- sub_df$Median_I_1 1 10 178402 1070.0
- sub_df$Vacant_R_1 1 58 178450 1070.0
- sub_df$Educatio_1 1 181 178573 1070.1
- sub_df$House_Va_1 1 1383 179775 1071.1
<none> 178392 1072.0
- sub_df$Employ_R_1 1 8984 187376 1077.3
- sub_df$House_De_1 1 11887 190279 1079.5
- sub_df$Poverty__1 1 14435 192827 1081.5
- sub_df$Room_Num_1 1 14558 192950 1081.6
- sub_df$Pop_Dens_1 1 39829 218221 1099.8
- sub_df$Mean_NTL 1 185412 363804 1175.5
Step: AIC=1070
sub_df$X2018_Prope ~ sub_df$Mean_NTL + sub_df$Pop_Dens_1 + sub_df$Employ_R_1 +
sub_df$Poverty__1 + sub_df$Educatio_1 + sub_df$House_De_1 +
sub_df$Vacant_R_1 + sub_df$House_Va_1 + sub_df$Room_Num_1
Df Sum of Sq RSS AIC
- sub_df$Vacant_R_1 1 60 178462 1068.0
- sub_df$Educatio_1 1 173 178575 1068.1
<none> 178402 1070.0
- sub_df$House_Va_1 1 2473 180875 1070.0
- sub_df$Employ_R_1 1 9063 187465 1075.3
- sub_df$House_De_1 1 11906 190308 1077.6
- sub_df$Poverty__1 1 15040 193442 1080.0
- sub_df$Room_Num_1 1 20818 199220 1084.3
- sub_df$Pop_Dens_1 1 39986 218388 1097.9
- sub_df$Mean_NTL 1 193486 371888 1176.7
Step: AIC=1068.05
sub_df$X2018_Prope ~ sub_df$Mean_NTL + sub_df$Pop_Dens_1 + sub_df$Employ_R_1 +
sub_df$Poverty__1 + sub_df$Educatio_1 + sub_df$House_De_1 +
sub_df$House_Va_1 + sub_df$Room_Num_1
Df Sum of Sq RSS AIC
- sub_df$Educatio_1 1 195 178657 1066.2
<none> 178462 1068.0
- sub_df$House_Va_1 1 2699 181160 1068.3
- sub_df$Employ_R_1 1 9101 187563 1073.4
- sub_df$House_De_1 1 12206 190668 1075.8
- sub_df$Poverty__1 1 15068 193530 1078.0
- sub_df$Room_Num_1 1 22733 201194 1083.8
- sub_df$Pop_Dens_1 1 41549 220011 1097.0
- sub_df$Mean_NTL 1 195825 374286 1175.7
Step: AIC=1066.21
sub_df$X2018_Prope ~ sub_df$Mean_NTL + sub_df$Pop_Dens_1 + sub_df$Employ_R_1 +
sub_df$Poverty__1 + sub_df$House_De_1 + sub_df$House_Va_1 +
sub_df$Room_Num_1
Df Sum of Sq RSS AIC
<none> 178657 1066.2
- sub_df$House_Va_1 1 2651 181308 1066.4
- sub_df$House_De_1 1 12253 190909 1074.0
- sub_df$Employ_R_1 1 16012 194669 1076.9
- sub_df$Poverty__1 1 17894 196551 1078.3
- sub_df$Room_Num_1 1 22675 201332 1081.9
- sub_df$Pop_Dens_1 1 41764 220421 1095.3
- sub_df$Mean_NTL 1 195709 374366 1173.7
reduced.model
Call:
lm(formula = sub_df$X2018_Prope ~ sub_df$Mean_NTL + sub_df$Pop_Dens_1 +
sub_df$Employ_R_1 + sub_df$Poverty__1 + sub_df$House_De_1 +
sub_df$House_Va_1 + sub_df$Room_Num_1)
Coefficients:
(Intercept) sub_df$Mean_NTL sub_df$Pop_Dens_1
2.505e+02 2.261e+00 -3.823e-01
sub_df$Employ_R_1 sub_df$Poverty__1 sub_df$House_De_1
1.699e+01 -7.092e+00 -4.535e-02
sub_df$House_Va_1 sub_df$Room_Num_1
-4.859e-05 -2.133e+01
full.model <- lm(sub_df$X2018_Larce ~ sub_df$Mean_NTL + sub_df$Pop_Dens_1 + sub_df$Employ_R_1 + sub_df$Poverty__1 + sub_df$Educatio_1 + sub_df$Median_I_1 + sub_df$House_De_1 + sub_df$Vacant_R_1 + sub_df$House_Va_1 + sub_df$Room_Num_1)
reduced.model <- step(full.model, direction = 'backward')
Start: AIC=1107.98
sub_df$X2018_Larce ~ sub_df$Mean_NTL + sub_df$Pop_Dens_1 + sub_df$Employ_R_1 +
sub_df$Poverty__1 + sub_df$Educatio_1 + sub_df$Median_I_1 +
sub_df$House_De_1 + sub_df$Vacant_R_1 + sub_df$House_Va_1 +
sub_df$Room_Num_1
Df Sum of Sq RSS AIC
- sub_df$Vacant_R_1 1 1 227497 1106.0
- sub_df$Median_I_1 1 1611 229107 1107.0
- sub_df$House_Va_1 1 2445 229941 1107.6
<none> 227496 1108.0
- sub_df$Educatio_1 1 3516 231012 1108.2
- sub_df$Employ_R_1 1 7685 235181 1110.9
- sub_df$Poverty__1 1 16615 244111 1116.4
- sub_df$House_De_1 1 32946 260442 1126.0
- sub_df$Room_Num_1 1 33898 261394 1126.5
- sub_df$Pop_Dens_1 1 43022 270518 1131.6
- sub_df$Mean_NTL 1 230404 457900 1209.5
Step: AIC=1105.98
sub_df$X2018_Larce ~ sub_df$Mean_NTL + sub_df$Pop_Dens_1 + sub_df$Employ_R_1 +
sub_df$Poverty__1 + sub_df$Educatio_1 + sub_df$Median_I_1 +
sub_df$House_De_1 + sub_df$House_Va_1 + sub_df$Room_Num_1
Df Sum of Sq RSS AIC
- sub_df$Median_I_1 1 1616 229113 1105.0
- sub_df$House_Va_1 1 2467 229964 1105.6
<none> 227497 1106.0
- sub_df$Educatio_1 1 3541 231039 1106.3
- sub_df$Employ_R_1 1 7685 235183 1108.9
- sub_df$Poverty__1 1 16616 244113 1114.4
- sub_df$House_De_1 1 34989 262487 1125.2
- sub_df$Room_Num_1 1 37626 265123 1126.6
- sub_df$Pop_Dens_1 1 44050 271547 1130.2
- sub_df$Mean_NTL 1 234660 462157 1208.9
Step: AIC=1105.02
sub_df$X2018_Larce ~ sub_df$Mean_NTL + sub_df$Pop_Dens_1 + sub_df$Employ_R_1 +
sub_df$Poverty__1 + sub_df$Educatio_1 + sub_df$House_De_1 +
sub_df$House_Va_1 + sub_df$Room_Num_1
Df Sum of Sq RSS AIC
- sub_df$House_Va_1 1 987 230101 1103.7
<none> 229113 1105.0
- sub_df$Educatio_1 1 5924 235037 1106.8
- sub_df$Employ_R_1 1 8789 237902 1108.6
- sub_df$Poverty__1 1 20084 249197 1115.5
- sub_df$House_De_1 1 36623 265736 1125.0
- sub_df$Room_Num_1 1 42176 271289 1128.0
- sub_df$Pop_Dens_1 1 43254 272367 1128.6
- sub_df$Mean_NTL 1 236075 465188 1207.8
Step: AIC=1103.66
sub_df$X2018_Larce ~ sub_df$Mean_NTL + sub_df$Pop_Dens_1 + sub_df$Employ_R_1 +
sub_df$Poverty__1 + sub_df$Educatio_1 + sub_df$House_De_1 +
sub_df$Room_Num_1
Df Sum of Sq RSS AIC
<none> 230101 1103.7
- sub_df$Educatio_1 1 4971 235071 1104.8
- sub_df$Employ_R_1 1 9436 239536 1107.6
- sub_df$Poverty__1 1 19691 249791 1113.8
- sub_df$House_De_1 1 40637 270738 1125.7
- sub_df$Pop_Dens_1 1 42643 272743 1126.8
- sub_df$Room_Num_1 1 84881 314981 1148.1
- sub_df$Mean_NTL 1 251144 481245 1210.9
reduced.model
Call:
lm(formula = sub_df$X2018_Larce ~ sub_df$Mean_NTL + sub_df$Pop_Dens_1 +
sub_df$Employ_R_1 + sub_df$Poverty__1 + sub_df$Educatio_1 +
sub_df$House_De_1 + sub_df$Room_Num_1)
Coefficients:
(Intercept) sub_df$Mean_NTL sub_df$Pop_Dens_1
309.98116 2.43673 -0.38584
sub_df$Employ_R_1 sub_df$Poverty__1 sub_df$Educatio_1
15.88780 -7.81962 6.19976
sub_df$House_De_1 sub_df$Room_Num_1
-0.08095 -31.90863
full.model <- lm(sub_df$X2018_Motor ~ sub_df$Mean_NTL + sub_df$Pop_Dens_1 + sub_df$Employ_R_1 + sub_df$Poverty__1 + sub_df$Educatio_1 + sub_df$Median_I_1 + sub_df$House_De_1 + sub_df$Vacant_R_1 + sub_df$House_Va_1 + sub_df$Room_Num_1)
reduced.model <- step(full.model, direction = 'backward')
Start: AIC=1104.45
sub_df$X2018_Motor ~ sub_df$Mean_NTL + sub_df$Pop_Dens_1 + sub_df$Employ_R_1 +
sub_df$Poverty__1 + sub_df$Educatio_1 + sub_df$Median_I_1 +
sub_df$House_De_1 + sub_df$Vacant_R_1 + sub_df$House_Va_1 +
sub_df$Room_Num_1
Df Sum of Sq RSS AIC
- sub_df$House_Va_1 1 110 222246 1102.5
- sub_df$House_De_1 1 120 222255 1102.5
- sub_df$Room_Num_1 1 530 222665 1102.8
- sub_df$Median_I_1 1 1370 223505 1103.4
<none> 222135 1104.5
- sub_df$Educatio_1 1 3420 225555 1104.7
- sub_df$Employ_R_1 1 3554 225690 1104.8
- sub_df$Poverty__1 1 4249 226384 1105.2
- sub_df$Vacant_R_1 1 7507 229642 1107.4
- sub_df$Pop_Dens_1 1 29271 251406 1120.8
- sub_df$Mean_NTL 1 121178 343313 1166.9
Step: AIC=1102.52
sub_df$X2018_Motor ~ sub_df$Mean_NTL + sub_df$Pop_Dens_1 + sub_df$Employ_R_1 +
sub_df$Poverty__1 + sub_df$Educatio_1 + sub_df$Median_I_1 +
sub_df$House_De_1 + sub_df$Vacant_R_1 + sub_df$Room_Num_1
Df Sum of Sq RSS AIC
- sub_df$House_De_1 1 189 222434 1100.7
- sub_df$Room_Num_1 1 588 222834 1100.9
<none> 222246 1102.5
- sub_df$Median_I_1 1 3053 225299 1102.5
- sub_df$Educatio_1 1 3702 225947 1103.0
- sub_df$Employ_R_1 1 3832 226078 1103.0
- sub_df$Poverty__1 1 4433 226679 1103.4
- sub_df$Vacant_R_1 1 7834 230080 1105.7
- sub_df$Pop_Dens_1 1 29198 251444 1118.8
- sub_df$Mean_NTL 1 134156 356401 1170.4
Step: AIC=1100.65
sub_df$X2018_Motor ~ sub_df$Mean_NTL + sub_df$Pop_Dens_1 + sub_df$Employ_R_1 +
sub_df$Poverty__1 + sub_df$Educatio_1 + sub_df$Median_I_1 +
sub_df$Vacant_R_1 + sub_df$Room_Num_1
Df Sum of Sq RSS AIC
- sub_df$Room_Num_1 1 500 222934 1099.0
<none> 222434 1100.7
- sub_df$Median_I_1 1 3204 225638 1100.8
- sub_df$Educatio_1 1 3750 226184 1101.1
- sub_df$Employ_R_1 1 3912 226346 1101.2
- sub_df$Poverty__1 1 4985 227419 1101.9
- sub_df$Vacant_R_1 1 7676 230110 1103.7
- sub_df$Pop_Dens_1 1 53841 276276 1130.7
- sub_df$Mean_NTL 1 141529 363964 1171.5
Step: AIC=1098.98
sub_df$X2018_Motor ~ sub_df$Mean_NTL + sub_df$Pop_Dens_1 + sub_df$Employ_R_1 +
sub_df$Poverty__1 + sub_df$Educatio_1 + sub_df$Median_I_1 +
sub_df$Vacant_R_1
Df Sum of Sq RSS AIC
<none> 222934 1099.0
- sub_df$Educatio_1 1 3376 226311 1099.2
- sub_df$Employ_R_1 1 4928 227862 1100.2
- sub_df$Poverty__1 1 6029 228963 1100.9
- sub_df$Vacant_R_1 1 7180 230114 1101.7
- sub_df$Median_I_1 1 12771 235706 1105.2
- sub_df$Pop_Dens_1 1 53344 276278 1128.7
- sub_df$Mean_NTL 1 164534 387468 1178.8
reduced.model
Call:
lm(formula = sub_df$X2018_Motor ~ sub_df$Mean_NTL + sub_df$Pop_Dens_1 +
sub_df$Employ_R_1 + sub_df$Poverty__1 + sub_df$Educatio_1 +
sub_df$Median_I_1 + sub_df$Vacant_R_1)
Coefficients:
(Intercept) sub_df$Mean_NTL sub_df$Pop_Dens_1
1.042e+02 1.831e+00 -3.359e-01
sub_df$Employ_R_1 sub_df$Poverty__1 sub_df$Educatio_1
1.114e+01 -4.230e+00 -5.999e+00
sub_df$Median_I_1 sub_df$Vacant_R_1
-5.404e-04 -1.409e+02
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